upload openvino test

others_project/openvinotest/openvino_cls_test/class_indices.json

@@ -0,0 +1,7 @@
+{
+    "0": "daisy",
+    "1": "dandelion",
+    "2": "roses",
+    "3": "sunflowers",
+    "4": "tulips"
+}

others_project/openvinotest/openvino_cls_test/main.py

@@ -0,0 +1,92 @@
+import sys
+import cv2
+import os
+import glob
+import json
+import numpy as np
+import logging as log
+from openvino.inference_engine import IECore
+
+
+def main():
+    device = "CPU"
+    model_xml_path = "./resnet34.xml"
+    model_bin_path = "./resnet34.bin"
+    image_path = "./"
+    class_json_path = './class_indices.json'
+
+    # set log format
+    log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
+
+    assert os.path.exists(model_xml_path), ".xml file not exist..."
+    assert os.path.exists(model_bin_path), ".bin file not exist..."
+
+    # search *.jpg files
+    image_list = glob.glob(os.path.join(image_path, "*.jpg"))
+    assert len(image_list) > 0, "no image(.jpg) be found..."
+
+    # load class label
+    assert os.path.exists(class_json_path), "class_json_path not exist..."
+    json_file = open(class_json_path, 'r')
+    class_indict = json.load(json_file)
+
+    # inference engine
+    ie = IECore()
+
+    # read IR
+    net = ie.read_network(model=model_xml_path, weights=model_bin_path)
+    # load model
+    exec_net = ie.load_network(network=net, device_name=device)
+
+    # check supported layers for device
+    if device == "CPU":
+        supported_layers = ie.query_network(net, "CPU")
+        not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
+        if len(not_supported_layers) > 0:
+            log.error("device {} not support layers:\n {}".format(device,
+                                                                  ",".join(not_supported_layers)))
+            log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
+                      "or --cpu_extension command line argument")
+            sys.exit(1)
+
+    # get input and output name
+    input_blob = next(iter(net.input_info))
+    output_blob = next(iter(net.outputs))
+
+    # set batch size
+    batch_size = 1
+    net.batch_size = batch_size
+
+    # read and pre-process input images
+    n, c, h, w = net.input_info[input_blob].input_data.shape
+    # images = np.ndarray(shape=(n, c, h, w))
+    # inference every image
+    for i in range(len(image_list)):
+        image = cv2.imread(image_list[i])
+        if image.shape[:-1] != (h, w):
+            image = cv2.resize(image, (w, h))
+        # bgr(opencv default format) -> rgb
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        # pre-process
+        image = image / 255.
+        image = (image - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
+        # change data from HWC to CHW
+        image = image.transpose((2, 0, 1))
+        # add batch dimension
+        image = np.expand_dims(image, axis=0)
+
+        # start sync inference
+        res = exec_net.infer(inputs={input_blob: image})
+        prediction = np.squeeze(res[output_blob])
+
+        # np softmax process
+        prediction -= np.max(prediction, keepdims=True)  # 为了稳定地计算softmax概率， 一般会减掉最大元素
+        prediction = np.exp(prediction) / np.sum(np.exp(prediction), keepdims=True)
+        class_index = np.argmax(prediction, axis=0)
+        print("prediction: '{}'\nclass:{}  probability:{}\n".format(image_list[i],
+                                                                    class_indict[str(class_index)],
+                                                                    np.around(prediction[class_index]), 2))
+
+
+if __name__ == '__main__':
+    main()